def test_vgg19_2(self):
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
model1 = VGG19(self.s, model_table='VGG19', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=(103.939, 116.779, 123.68),
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir+'VGG_ILSVRC_19_layers.caffemodel.h5',
include_top=True)
model2 = VGG19(self.s, model_table='VGG19', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=None,
random_crop=None,
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir+'VGG_ILSVRC_19_layers.caffemodel.h5',
include_top=True)
self.assertRaises(ValueError,
lambda:VGG19(self.s, model_table='VGG19',
n_classes=1000, n_channels=3, width=224, height=224, scale=1,
offsets=None,
random_crop='wrong_val',
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir+'VGG_ILSVRC_19_layers.caffemodel.h5',
include_top=True))
def test_vgg19(self):
from dlpy.applications import VGG19
model = VGG19(self.s)
model.print_summary()
# test random_crop and mutation
model1 = VGG19(self.s, model_table='VGG16', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=None,
random_crop='unique',
random_flip='hv',
random_mutation='random',
include_top=True)
model1.print_summary()
res1 = self.s.fetch(table=model1.model_name, sortby='_dllayerid_')
print(res1)
self.assertEqual(res1['Fetch'].iloc[10, 3], 4)
self.assertEqual(res1['Fetch'].iloc[4, 3], 2)
def test_vgg19_2(self):
if self.data_dir is None:
unittest.TestCase.skipTest(self, "DLPY_DATA_DIR is not set in the environment variables")
file_dependency = self.data_dir + 'VGG_ILSVRC_19_layers.caffemodel.h5'
if not file_exist_on_server(self.s, file_dependency):
unittest.TestCase.skipTest(self, "File, {}, not found.".format(file_dependency))
model1 = VGG19(self.s, model_table='VGG19', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=(103.939, 116.779, 123.68),
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_19_layers.caffemodel.h5',
include_top=True)
model2 = VGG19(self.s, model_table='VGG19', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=None,
random_crop=None,
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_19_layers.caffemodel.h5',
include_top=True)
self.assertRaises(ValueError,
lambda: VGG19(self.s, model_table='VGG19',
n_classes=1000, n_channels=3, width=224, height=224, scale=1,
offsets=None,
random_crop='wrong_val',
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_19_layers.caffemodel.h5',
include_top=True))
# test random_crop and mutation
model3 = VGG19(self.s, model_table='VGG16', n_classes=1000, n_channels=3,
width=224, height=224, scale=1,
offsets=None,
random_crop='unique',
random_flip='hv',
random_mutation='random',
pre_trained_weights=True,
pre_trained_weights_file=self.data_dir + 'VGG_ILSVRC_16_layers.caffemodel.h5',
include_top=True)
model3.print_summary()
def test_vgg19(self):
from dlpy.applications import VGG19
model = VGG19(self.s)
model.print_summary()